Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

DeepSLAM: A Robust Monocular SLAM System with Unsupervised Deep Learning

In this paper, we propose DeepSLAM, a novel unsupervised deep learning-based visual Simultaneous Localization and Mapping (SLAM) system. The DeepSLAM training is fully unsupervised since it only requires stereo imagery instead of annotating ground-truth poses. Its testing takes a monocular image sequence as the input. Therefore, it is a monocular SLAM paradigm. DeepSLAM consists of several essential components, including Mapping-Net, Tracking-Net, Loop-Net and a graph optimization unit. Specifically, the Mapping-Net is an encoder and decoder architecture for describing the 3D structure of the environment while the Tracking-Net is a Recurrent Convolutional Neural Network (RCNN) architecture for capturing the camera motion. The Loop-Net is a pre-trained binary classifier for detecting loop closures. DeepSLAM can simultaneously generate pose estimate, depth map and outlier rejection mask. We evaluate its performance on various datasets, and find that DeepSLAM achieves good performance in terms of pose estimation accuracy, and is robust in some challenging scenes

Adhesives from biomass pyrolysis

Fast pyrolysis of waste biomass with high lignin content, such as birch wood, birch bark, hydrolysis lignin, kraft lignin or low-cost digestate from biogas production, provides oils that can be substituted for phenol in phenol-formaldehyde resins. Biomass fast pyrolysis was performed in a dedicated fluidized bed pyrolyzer that incorporated two crucial innovations: a fractional condensation train provided dry bio-oils with ~1% of moisture and much reduced acidity; autothermal pyrolysis with partial oxidation reduces operating and capital costs, as well as increasing the quality of the dry bio-oil. Dry bio-oil obtained from autothermal fast pyrolysis of kraft lignin can be used to substitute up to 80% of phenol in reacting with formaldehyde to produce wood adhesives that met International Standards, including specifications on the dry and wet mechanical strength and formaldehyde emission (Figure 1). With dry bio-oils from the low-cost residues (birch wood, birch bark, hydrolysis lignin, or digestate), the substitution level can be 50~65%. In addition, there is no need to change the hot press temperature or curing time from the current settings for pure phenol resin. The mechanical strength and formaldehyde emission levels of the bonded plywood are affected by the phenol substitution ratio, and the concentration and molecular weight of the phenolics in the dry bio-oil. Please click Additional Files below to see the full abstract

Indoor Relocalization in Challenging Environments With Dual-Stream Convolutional Neural Networks

This paper presents an indoor relocalization system using a dual-stream convolutional neural network (CNN) with both color images and depth images as the network inputs. Aiming at the pose regression problem, a deep neural network architecture for RGB-D images is introduced, a training method by stages for the dual-stream CNN is presented, different depth image encoding methods are discussed, and a novel encoding method is proposed. By introducing the range information into the network through a dual-stream architecture, we not only improved the relocalization accuracy by about 20% compared with the state-of-the-art deep learning method for pose regression, but also greatly enhanced the system robustness in challenging scenes such as large-scale, dynamic, fast movement, and night-time environments. To the best of our knowledge, this is the first work to solve the indoor relocalization problems based on deep CNNs with RGB-D camera. The method is first evaluated on the Microsoft 7-Scenes data set to show its advantage in accuracy compared with other CNNs. Large-scale indoor relocalization is further presented using our method. The experimental results show that 0.3 m in position and 4° in orientation accuracy could be obtained. Finally, this method is evaluated on challenging indoor data sets collected from motion capture system. The results show that the relocalization performance is hardly affected by dynamic objects, motion blur, or night-time environments

Computational Intelligence Approaches to Robotics, Automation, and Control

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Multi-layered map based navigation and interaction for an intelligent wheelchair

Intelligent wheelchair is a paradigm of assisted living applications for elderly and disabled people. Its autonomous navigation and human-robot interaction is the major challenge. The previous intelligent wheelchair research has been mainly focused on geometric map based navigation, which is computational expensive in a large scale environment. This paper proposes the use of multi-layered maps for navigation and interaction of an intelligent wheelchair. The semantic information can improve the efficiency of path planning and navigation as well as extend the capability of task planning for the wheelchair. Some experimental results are given to demonstrate the feasibility and performance of the proposed approach

Indoor Topological Localization Based on a Novel Deep Learning Technique

Millions of people in the world suffer from vision impairment or vision loss. Traditionally, they rely on guide sticks or dogs to move around and avoid potential obstacles. However, both guide sticks and dogs are passive. They are unable to provide conceptual knowledge or semantic contents of an environment. To address this issue, this paper presents a vision-based cognitive system to support the independence of visually impaired people. More specifically, a 3D indoor semantic map is firstly constructed with a hand-held RGB-D sensor. The constructed map is then deployed for indoor topological localization. Convolutional neural networks are used for both semantic information extraction and location inference. Semantic information is used to further verify localization results and eliminate errors. The topological localization performance can be effectively improved despite significant appearance changes within an environment. Experiments have been conducted to demonstrate that the proposed method can increase both localization accuracy and recall rates. The proposed system can be potentially deployed by visually impaired people to move around safely and have independent life

Using Unsupervised Deep Learning Technique for Monocular Visual Odometry

Deep learning technique-based visual odometry systems have recently shown promising results compared to feature matching-based methods. However, deep learning-based systems still require the ground truth poses for training and the additional knowledge to obtain absolute scale from monocular images for reconstruction. To address these issues, this paper presents a novel visual odometry system based on a recurrent convolutional neural network. The system employs an unsupervised end-to-end training approach. The depth information of scenes is used alongside monocular images to train the network in order to inject scale. Poses are inferred only from monocular images, thus making the proposed visual odometry system a monocular one. The experiments are conducted and the results show that the proposed method performs better than other monocular visual odometry systems. This paper has made two main contributions: 1) the creation of the unsupervised training framework in which the camera ground truth poses are only deployed for system performance evaluation rather than for training and 2) the absolute scale could be recovered without the post-processing of poses